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Volumn , Issue , 2006, Pages 67-74

Continuous decision MTE influence diagrams

Author keywords

[No Author keywords available]

Indexed keywords

DECISION PROBLEMS; DECISION RULES; DECISION VARIABLES; DISCRETE DECISION VARIABLES; EXPECTED UTILITY; INFLUENCE DIAGRAM; MIXTURES OF TRUNCATED EXPONENTIALS; PROBABILITY THEORY; UTILITY FUNCTIONS;

EID: 79952700463     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (3)

References (14)
  • 1
    • 84874723328 scopus 로고    scopus 로고
    • Working Paper, Virginia ilitary Institute. Available for download at
    • Cobb, B.R. (2006), "Refining decision rules in influence diagrams, " Working Paper, Virginia ilitary Institute. Available for download at: www.vmi.edu/media/ecbu/cobb/CONTID.pdf
    • (2006) Refining Decision Rules in Influence Diagrams
    • Cobb, B.R.1
  • 2
    • 34047168049 scopus 로고    scopus 로고
    • Hybrid influence diagrams using mixtures of truncated exponentials
    • M. Chickering and J. Halpern (eds.)
    • Cobb, B.R. and RR Shenoy (2004), "Hybrid influence diagrams using mixtures of truncated exponentials, " in M. Chickering and J. Halpern (eds.) Uncertainty in Artificial Intelligence, 20, 85-93.
    • (2004) Uncertainty in Artificial Intelligence , vol.20 , pp. 85-93
    • Cobb, B.R.1    Shenoy, R.R.2
  • 3
    • 33646438850 scopus 로고    scopus 로고
    • Hybrid Bayesian networks with linear deterministic vari ables
    • F. Bacchus and T. Jaakkola (eds.)
    • Cobb, B.R. and P.P. Shenoy (2005), "Hybrid Bayesian networks with linear deterministic vari ables, " in F. Bacchus and T. Jaakkola (eds.), Uncertainty in Artificial Intelligence, 21, 136-144.
    • (2005) Uncertainty in Artificial Intelligence , vol.21 , pp. 136-144
    • Cobb, B.R.1    Shenoy, P.P.2
  • 4
    • 33644863766 scopus 로고    scopus 로고
    • Inference in hybrid Bayesian networks using mixtures of truncated exponentials
    • Cobb, B.R. and P.P. Shenoy (2006a), "Inference in hybrid Bayesian networks using mixtures of truncated exponentials, International Journal of Approximate Reasoning, 41(3), 257-286.
    • (2006) International Journal of Approximate Reasoning , vol.41 , Issue.3 , pp. 257-286
    • Cobb, B.R.1    Shenoy, P.P.2
  • 5
    • 33745634852 scopus 로고    scopus 로고
    • Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials
    • Cobb, B.R., Shenoy, P.P. and R. Rumi (2006), "Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials, " Statistics and Computing 16(3), 293-308.
    • (2006) Statistics and Computing , vol.16 , Issue.3 , pp. 293-308
    • Cobb, B.R.1    Shenoy, P.P.2    Rumi, R.3
  • 7
    • 10844270316 scopus 로고    scopus 로고
    • Solving linear-quadratic conditional Gaussian influence diagrams
    • Adsen, A.L. and F. Jensen (2005), "Solving linear-quadratic conditional Gaussian influence diagrams, International Journal of Approximate Reasoning, 38(3), 263-282.
    • (2005) International Journal of Approximate Reasoning , vol.38 , Issue.3 , pp. 263-282
    • Adsen, A.L.1    Jensen, F.2
  • 8
    • 0006310869 scopus 로고    scopus 로고
    • Lazy evaluation of symmetric Bayesian decision problems
    • K.B. Laskey and H. Prade (eds.)
    • Adsen, A.L. and F.V. Jensen (1999), "Lazy evaluation of symmetric Bayesian decision problems, in K.B. Laskey and H. Prade (eds.), Uncertainty in Artificial Intelligence, 15, 382-390.
    • (1999) Uncertainty in Artificial Intelligence , vol.15 , pp. 382-390
    • Adsen, A.L.1    Jensen, F.V.2
  • 9
    • 0003473088 scopus 로고
    • Ph.D. Thesis, Department of Engineering-Economic Systems, Stanford Univer sity, Stanford, CA
    • Olmsted, S.M. (1983), "On representing and solving decision problems, " Ph.D. Thesis, Department of Engineering-Economic Systems, Stanford Univer sity, Stanford, CA.
    • (1983) On Representing and Solving Decision Problems
    • Olmsted, S.M.1
  • 10
    • 21244504129 scopus 로고
    • Mixtures of Gaussians and minimum relative entropy techniques for modeling continuous uncertainties
    • D. Heckerman and E.H. Mamdani (eds.)
    • Poland, W.B. and R.D. Shachter (1993), "Mixtures of Gaussians and minimum relative entropy techniques for modeling continuous uncertainties, " in D. Heckerman and E.H. Mamdani (eds.), Uncertainty in Artificial Intelligence, 9, 183-190.
    • (1993) Uncertainty in Artificial Intelligence , vol.9 , pp. 183-190
    • Poland, W.B.1    Shachter, R.D.2
  • 12
    • 0022818911 scopus 로고
    • Evaluating influence diagrams
    • Shachter, R.D. (1986), "Evaluating influence diagrams, " Operations Research, 34(6), 871-882.
    • (1986) Operations Research , vol.34 , Issue.6 , pp. 871-882
    • Shachter, R.D.1
  • 13
    • 0000987741 scopus 로고
    • Gaussian influence diagrams
    • Shachter, R.D. and CR. Kenley (1989), "Gaussian influence diagrams, " Management cience, 35(5), 527-550.
    • (1989) Management Cience , vol.35 , Issue.5 , pp. 527-550
    • Shachter, R.D.1    Kenley, C.R.2
  • 14
    • 0011915858 scopus 로고
    • A new method for represent ing and solving Bayesian decision problems
    • DJ. Hand (ed.) Chapman and Hall, London
    • Shenoy, P.P. (1993), "A new method for represent ing and solving Bayesian decision problems, " in DJ. Hand (ed.), Artificial Intelligence Frontiers in Statistics: AI and Statistics III, Chapman and Hall, London, 119-138.
    • (1993) Artificial Intelligence Frontiers in Statistics: AI and Statistics , vol.3 , pp. 119-138
    • Shenoy, P.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.